谷歌无人驾驶月度实测数据2015年12月

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谷歌开发自动驾驶汽车技术以减少交通事故

随着中国新农村建设的深入，一些新问题也
应运而生。如农民住宅实行统一规划、统一设
（３以上则来源于美国加州大学洛杉矶分校
周江评，２１ —１ —１）００１０
计、统一建设和统一配套基础设施后，农民的交
机场停车策略评估和相关技术在中国，伴随着小汽车越来越多地进入家庭，人们也在更多的场所需要停车场地，例如机
巴士转换到自行车这一有效的方式来降低二氧化
碳的排放量。
（经济参考报，２１ —１ —１）００１２
场、火车站、汽车站等城市重要对外交通节点。
素。人们更加关注气候变化，通过从小汽车或者
趣的读者，可访问：
ｈｐ／ｐ．．ｇｃｆｄＴＢｔｒｅｔｓｌ．ｐｔ：ａｓｒｏ／ｅ／ＲＮｅｏｃｐｙｓ？ｔ／ｐｔｒｍｓｅｂＰｊＤｉａａＰｏｅｔ＝９７ｒｃＤ２８。ｊｌ
年均２％～０５３％的速度增长，近２％的成年人使用０头盔，９岁以下儿童的头盔使用率高达７％。５
利用与交通关联的研究常常默认是针对城市地区，而在美国，类似的现象也不同程度地存在。最近，美国交通研究会为改变这一现象，提供了６万美元开放性资金，面向全美研究人员招标，５
世界各国研究者从不同侧面研究交通行为的ｌ篇８
间，未来还能实现汽车共享，并大大降低汽车使用，帮助创造 “ 未来公路列车” 。

综述无人驾驶汽车的主要功能

综述无人驾驶汽车的主要功能摘要：随着技术模式的不断更迭与改进，无人驾驶已成为世界各国竞相投入开发的新技术。

原因在于无人驾驶技术涉及多项领域，其中包括无人控制，人工智能，计算机视觉等高新技术，还与互联网紧密结合。

将无人驾驶技术做好做强，掌握的不仅是一门精尖技术，更是对本国科技水平的一次整合与提升。

本文简要介绍了无人驾驶汽车的一些主要功能及实现方法，对无人驾驶的一些问题进行了思考，简述了无人驾驶技术的一些优势和不足。

关键词：无人驾驶、路线规划、车内信息采集、车外信息采集1. 引言无人驾驶汽车在20世纪已经有了数十年的历史，在21世纪呈现出了实用趋势，国内外几家公司也在快速推进无人驾驶汽车实用化。

当前，人们对无人驾驶汽车的需求不断提高，无人驾驶技术也日益成熟。

因此开展对无人驾驶方面的研究具有重要的意义。

2. 无人驾驶技术的简介2.1 无人驾驶汽车的发展情况无人驾驶这一概念最先由英、德、美等发达国家在上世纪七八十年代提出。

发展至今，国外的许多公司都取得了不错的成绩。

如谷歌，其在2014年12月中下旬，就首次展示了无人驾驶原型车成品，该车可全功能运行。

2015年5月8日在美国内华达州允许测试3个月后，谷歌的无人驾驶汽车就取得了合法牌照。

在我国，无人驾驶技术日臻成熟。

在1992年国防科技大学成功研制出中国第一辆真正意义上的无人驾驶汽车。

现今，百度与北汽合作打造的无人驾驶汽车已经试运营，并且达到L3级别。

未来，我国将推进立法，使无人驾驶汽车真正合法化。

美国汽车工程师学会（SAE）对自动驾驶有明确的分级并已经被 NHTSA 确定为标准，从辅助驾驶到完全不需要人干预的自动驾驶有明确界定[1]。

根据NHTSA对无人驾驶汽车的分级，无人驾驶汽车分为L0、L1、L2、L3、L4五个等级，如表1。

美国摩根斯丹利研究报告中指出在2025 年自动驾驶技术在美国的潜在经济影响为 2000 亿到 1.9万亿美元左右。

[2]2.2 无人汽车的组成部分无人汽车的组成部分主要有：GPS、测距雷达、激光发射器、中央处理器、视频采集器。

谷歌无人驾驶月度实测数据2016年3月

ACTIVITY SUMMARYAll metrics as of March 31, 2016Vehicles:●21 Lexus RX450h SUVs currently self-driving on public streets: 13 in Mountain View, CA; 8 inAustin, TX●33 prototypes currently self-driving on public streets: 24 in Mountain View, CA; 7 in Austin, TX; 2in Kirkland, WAMiles driven since start of project in 2009“Autonomous mode” means the software is driving the vehicle, and test drivers are not touching the manual controls. “Manual mode” means the test drivers are driving the car.●Autonomous mode:1,498,214●Manual mode:1,046,386●We average around 10,000—15,000 autonomous miles per week on public streets BUILDING MAPS FOR A SELF-DRIVINGCARWe’re often asked how we build mapsspecifically for a fully autonomous car. A mapfor self-driving cars has a lot more detail thanconventional maps (e.g. the height of a curb,width of an intersection, and the exact locationof a traffic light or stop sign), so we’ve had todevelop a whole new way of mapping theworld.Before we drive in a new city or new part oftown, we build a detailed picture of what’saround us using the sensors on our self-drivingcar. As we drive around town, our lasers sendout pulses of light that help us paint a three-dimensional portrait of the world. We’re able to tell the distance and dimensions of road features based on the amount of time it takes for the laser beam to bounce back to our sensors (see image above). Our mapping team then turns this into useful information for our cars by categorizing interesting features on the road, such as driveways, fire hydrants, and intersections.This level of detail helps our car know exactly where it is in the world. As our cars drive autonomously on the road, our software matches what the car sees in real-time with the maps we’ve already built, allowing the car to know its position on the road to within 10cm of accuracy. That means we don’t have to rely on GPS technology, or a single point of data such as lane markings, to navigate the streets. Another benefit of knowing permanent features of the road is that our sensors and software can focus more on moving objects, like pedestrians, vehicles, and construction zones. This allows us to do a better job of anticipating — and avoiding — tricky situations.Self-driving cars can use a much greater level of detail than you’d find on Google Maps. Our mapping team highlights road features such as the length of a crosswalk, height of a traffic light, and the curve of a turn.Of course our streets are ever-changing, so our cars need to be able to recognize new conditions and make adjustments in real-time. For example, we can detect signs of construction (orange cones, workmen in vests, etc.) and understand that we may have to merge to bypass a closed lane, or that other road users may behave differently.To keep our maps up-to-date, our cars automatically send reports back to our mapping team whenever they detect changes like these. The team can then quickly update the map and share information with the whole autonomous fleet.SCENES FROM THE STREETEach month we’ll give examples of situations we encounter on the roadWhat do a 1980s Japanese arcade gameand our self-driving car have in common? This month we showed a compilation of odd encounters we’ve recently had on the streets while out testing. One of these included half a dozen people leap-frogging through traffic in front of one of our self-driving cars (if you’re finding that difficult to imagine, you can watch Chris Urmson show this encounter in his S XSW speechat 26:00).Despite never encountering humans posing as an army of frogs, our car still knew how to behave safely (though your parents would probably tell you this is unsafe behavior anyway, so kids don't try this at home!). That’s because rather than teaching the car to handle very specific things, we give the car fundamental capabilities for detecting other road users or unfamiliar objects, and then we give it lots of practice in a wide range of situations.On our private test track, we’ve dreamt up and recreated hundreds of odd scenarios to gauge our car’s response (e.g we even had someone jump out of a porta potty on the side of the road), but situations like these demonstrate why public testing of our self-driving cars is important to developing our cars for the road. We can try to come up with lots of wacky situations for our cars to handle, but the real world can defy even our wildest imaginations.TRAFFIC COLLISIONS INVOLVING AUTONOMOUS FLEETIn this section, we detail any accidents our self-driving fleet has been involved in while testing on public roads. Given the time we’re spending on busy streets, we’ll inevitably be involved in collisions; sometimes it’s impossible to overcome the realities of speed and distance. Thousands of minor accidents happen every day on typical American streets, 94% of them involving human error, and as many as 55% of them go unreported. (And we think this number is low; for more, see h ere.)March 14, 2016:A Google Lexus-model autonomous vehicle (“Google AV”) travelling westbound on W. Anderson Ln. in Austin, TX in autonomous mode was rear-ended while stopped at a traffic light. The Google AV was stopped for approximately 3 seconds behind traffic waiting at a red light at Burnet Road, when a vehicle (Volkswagen Passat) approaching from behind collided with the rear bumper of the Google AV. The Google AV’s speed at the time of the collision was 0 mph. The other vehicle’s approximate speed at the time of the collision was 10 mph.The driver of the other vehicle appeared disoriented to the Google AV test driver, so the Google AV test driver called 911, and the 911 dispatcher sent emergency vehicles to the scene. The Google AV sustained minor damage to its rear bumper. The other vehicle sustained moderate damage to its front bumper.WHAT WE’VE BEEN READING●SxSW: W atch Chris Urmson explain Google’s self-driving car project [video](March 2016)●Washington Post: I rode in Google’s self-driving car. This what impressed me the most.(March2016)●The Verge:G oogle's bus crash is changing the conversation around self-driving cars(March2016)●USA Today:S elf-driving car leaders ask for national laws(March 2016)●AP:A utonomous cars aren't perfect, but how safe must they be?(March 2016)。

四川省2020年公需科目试题及答案

四川省2020年（专业技术人员）公需科目试题及答案第1 题（单选）《“十二五”期间深化医药卫生体制改革规划暨实施方案》提出，到2015年，非公立医疗机构床位数和服务量达到总量的（B）。

第2 题（单选）（D）中提出“坚持创新驱动发展，加快大数据部署，深化大数据应用，已成为稳增长、促改革、调结构、惠民生和推动政府治理能力现代化的内在需要和必然选择。

”第3 题（单选）本讲提到，（D）是指没有表现在失业数据中的失业，即就业状态的低效率。

第4 题（单选）根据本讲，在两化融合的四个基本要素中，（C）是一个非常重要的驱动要素。

它强调信息资源管理和数据的开发利用。

第5 题（单选）（B）可以预测经济违约概率。

第6 题（单选）欧盟于2013年提出了（C），该计划项目为期10年，欧盟和参与国将提供近12亿欧元经费，使其成为了全球范围内最重要的人类大脑研究项目。

第7 题（单选）根据本讲，技术、业务流程、组织结构和数据是两化融合管理体系框架中的（D ）。

第8 题（单选）根据本讲，一些企业实行末位淘汰制是由于（D）的信息智能时代工作特征。

第9 题（单选）关于中国人工智能产业技术创新日益活跃,下列说法不正确的是（A）。

第10 题（单选）（B）是指大学、科研机构与企业在人力资本层面进行交流互动，以促进人工智能产学研各方的知识交流和知识创新。

第11 题（单选）根据本讲，两化融合管理体系和其他管理体系是一种（D）的管理模式。

第12 题（单选）根据本讲，西方发达国家是先实现（C），后实现（）。

第13 题（单选）国内大多数语音识别技术商都在（B）的方向上发力。

第14 题（单选）（C）推出了人工智能开放平台，围绕智能汽车和智能家居，打造了Apollo和DuerOS两大行业开放生态，加速推动无人驾驶汽车和智能家居迈向世界先进水平。

第15 题（单选）2017年我国民营医院服务量只占全国医院的（D）。

第16 题（单选）下列有关人工智能的说法中，不正确的是（C）。

谷歌自动驾驶测试报告(2015年12月)

Google Self-Driving Car Testing Reporton Disengagements of Autonomous ModeDecember 2015IntroductionIn accordance with regulations issued by the the California Department of Motor Vehicles (DMV), Google Auto LLC (“Google”) submits this report of disengagements from autonomous mode that have occurred when operating its self-driving cars (SDCs) on public roads in California. In accordance with the DMV rule , this report covers the period from the date of issuance of Google’s1Manufacturer’s Testing Permit (September 24, 2014) through November 30, 2015.As of the end of November, Google had operated its self-driving cars in autonomous mode for more than 1.3 million miles. Of those miles, 424,331 occurred on public roads in California during the period covered by this report -- with the vast majority on surface streets in the typical suburban city environment of Mountain View, CA and neighboring communities. We’re self-driving 30,000-40,000 miles or more per month, which is equal to two to four years of typical US adult driving.The setting in which our SDCs and our drivers operate most frequently is important. M astering autonomous driving on city streets -- rather than freeways, interstates or highways -- requires us to navigate complex road environments such as m ulti-lane intersectionsor unprotected left-hand turns, a larger variety of road users including cyclists and pedestrians, and more unpredictable behavior from other road users. This differs from the driving undertaken by an average American driver who will spend a larger proportion of their driving miles on less complex roads such as freeways. N ot surprisingly, 89 percent of our reportable disengagements have occurred in this complex street environment (see Table 6 below).Disengagements are a critical part of the testing process that allows our engineers to expand the software’s capabilities and identify areas of improvement. Our objective is not to minimize disengagements; rather, it is to gather, while operating safely, as much data as possible to enable us to improve our self-driving system. Therefore, we set disengagement thresholds conservatively, and each is carefully recorded. We have an evaluation process in which we identify disengagements that may signal any safety issues, and we resolve them by refining our software, firmware, or hardware and incorporating those changes across our entire fleet.As we continue to develop our technology, the rate of safety significant disengagements has fallen even as we drive more autonomous miles on public roads.Disengagements Covered by This ReportThe DMV rule defines disengagements as deactivations of the autonomous mode in two situations: (1) “when a failure of the autonomous technology is detected,” or (2) “when the safe operation of the vehicle requires that the autonomous vehicle test driver disengage the autonomous mode and take immediate manual control of the vehicle.” In adopting this definition, the DMV noted:1 S ection 227.46 of Article 3.7 (Autonomous Vehicles) of Title 13, Division 1, Chapter 1, California Code of Regulations“This clarification is necessary to ensure that manufacturers are not reporting each common or routine disengagement.”2As part of testing, our cars switch in and out of autonomous mode many times a day. These disengagements number in the many thousands on an annual basis though the vast majority are considered routine and not related to safety. Safety is our highest priority and Google test drivers are trained to take manual control in a multitude of situations, not only when safe operation “requires” that they do so. Our drivers err on the side of caution and take manual control if they have any doubt about the safety of continuing in autonomous mode (for example, due to the behavior of the SDC or any other vehicle, pedestrian, or cyclist nearby), or in situations where other concerns may warrant manual control, such as improving ride comfort or smoothing traffic flow. Similarly, the SDC’s computer hands over control to the driver in many situations that do not involve a “failure of the autonomous technology” and do not require an immediate takeover of control by the driver. We explain more in each relevant section below.Failure of the Autonomous Technology DetectedIn events where the software has detected a technology “failure” -- i.e. an issue with the autonomous technology that may affect the safe operation of the vehicle -- the SDC will immediately hand over control to the driver; we categorize these as “immediate manual control” disengagements. In these cases, the test driver is given a distinct audio and visual signal, indicating that immediate takeover is required.3“Immediate manual control” disengage thresholds are set conservatively. Our objective is not to minimize disengages; rather, it is to gather as much data as possible to enable us to improve our self-driving system. Our self-driving system runs thousands of checks on itself every second. Immediate manual control disengages are triggered primarily when we detect a communication failure between the primary and secondary (back-up) self-driving systems (for example, a broken wire); when we detect anomalies in sensor readings related to our acceleration or position in the world (accelerometers or GPS); or when we detect anomalies in the monitoring of key functions like steering and braking.During the reporting period, Google’s fleet of SDCs experienced 272 such disengagements. Our test drivers are trained and prepared for these events and the average driver response time of all measurable events was 0.84 seconds.As we continue to develop and refine the self-driving software, we are seeing fewer disengagements of this type despite a growing number of miles driven each month (Table 1). The number of autonomous miles we are driving between immediate manual control disengagements is increasing steadily over time. The rate of this type of disengagement has dropped significantly from 785 miles per disengagement in the fourth quarter of 2014 to 5318 miles per disengagement in the fourth quarter of 2015. Figure 1 illustrates this improvement.2 DMV’s Final Statement of Reasons at page 2.3 D uring this testing phase of the software, our SDC hands over control to test drivers on many other occasions that are not “failures” of the autonomous technology. As we calibrate our software and hardware, we closely monitor its performance and alert our drivers and engineers to any minor anomalies.Table 1: Disengagements related to detection of a failure of the autonomous technologyMonthNumberDisengagesAutonomous mileson public roads2014/09 0 4207.22014/10 14 23971.12014/11 14 15836.62014/12 40 9413.12015/01 48 18192.12015/02 12 18745.12015/03 26 22204.22015/04 47 31927.32015/05 9 38016.82015/06 7 42046.62015/07 19 34805.12015/08 4 38219.82015/09 15 36326.62015/10 11 47143.52015/11 6 43275.9Total 272 424331Figure 1: Autonomous miles driven per disengagement related to detection of a failure of the autonomous technologyDisengagements Where Safe Operation of the Vehicle Requires Control by the DriverOur test drivers play a critical role in refining our technology and ensuring the safe operation of the vehicles while we are in this development phase. They are directed to take control of the vehicle as often as they feel is necessary and for a variety of reasons relating to the comfort of the ride, the safety of the vehicle, or the erratic or unpredictable behavior of other road users.Each time a test driver takes manual control of the vehicle, our system automatically records the circumstances leading up to the disengagement from autonomous mode and flags them for review by the software team. This information, along with feedback given by the test driver, is used to evaluate the software for any potential safety issues or areas of improvement, such as making our self-driving car drive more smoothly.To help evaluate the significance of driver disengagements, we employ a powerful simulator program -- developed in-house by our engineers -- that allows the team to “replay” each incident and predict the behavior of the self-driving car (had the driver not taken control of it) as well as the behavior and positions of other road users in the vicinity (such as pedestrians, cyclists, and other vehicles). The simulator can also create thousands of variations on that core event so we can evaluatewhat would have happened under slightly different circumstances, such as our vehicle and other road users moving at different times, speeds, and angles.Through this process we can determine the events that have safety significance and should receive prompt and thorough attention from our engineers in resolving them. In the reporting period, there were 69 events across our fleet in which safe operation of the vehicle required disengagement by the driver.Each of these events is carefully studied to root out the underlying issue or family of issues, and our software is then refined. The revised software is tested extensively, in simulation, on closed courses and on public roads with our test drivers. Even with the vast majority of our autonomous miles being driven in complex city street environments, we only record a few safe operation disengagements each month (Table 2) .Table 2: Driver-initiated disengagements related to safe operation of the vehicleMonthNumberDisengagesAutonomous mileson public roads2014/09 2 4207.22014/10 5 23971.12014/11 7 15836.62014/12 3 9413.12015/01 5 18192.12015/02 2 18745.12015/03 4 22204.22015/04 4 31927.32015/05 4 38016.82015/06 4 42046.62015/07 10 34805.12015/08 3 38219.82015/09 1 36326.62015/10 5 47143.52015/11 10 43275.9Total69 424331Figure 2, below, displays how the number of autonomous miles driven between such disengagements has changed over the calendar quarters covered in the report. The low absolute number of events makes a trend hard to discern because an aberrational month can skew the data.Figure 2: Autonomous miles driven per driver-initiated disengagement related to safe operationof the vehicleOf the 69 reportable safe operation events, 13 were “simulated contacts” -- events in which, upon replaying the event in our simulator, we determined that the test driver prevented our vehicle from making contact with another object. The remaining 56 of the 69 events were safety-significant because, under simulation, we identified some aspect of the SDC’s behavior that could be a potential cause of contacts in other environments or situations if not addressed. This includes proper perception of traffic lights, yielding properly to pedestrians and cyclists, and violations of traffic laws. To be clear, however, these 56 events during the reporting period would very likely not have resulted in a real-world contact if the test driver had not taken over.In 10 of the 13 simulated contact events, the SDC’s predicted behavior would have, in simulation, caused contact (though 2 of these involved simulated contact with traffic cones). In 3 of the 13 occasions, a driver in another vehicle made a move that would have, in simulation, caused a contact with our car (e.g., in one case the other vehicle was driving the wrong way down the road in the SDC’s path); in these cases, we believe a human driver could have taken a reasonable action to avoid the contact but the simulation indicated the SDC would not have taken that action.These events are rare and our engineers carefully study these simulated contacts and refine the software to ensure the self-driving car performs safely. A software “fix” is tested against many miles of simulated driving, then tested on the road, and, after careful review and validation, rolled out to the entire fleet. The rate of these simulated contact disengagements is declining even as autonomous miles driven increase. Because the simulated contact events are so few in number, they do not lend themselves well to trend analysis, but, we are generally driving more autonomous miles between these events. From April 2015 to November 2015, our cars self-drove more than 230,000 miles without a single such event.Table 3: Disengagements related to simulated contacts of the autonomous technologyMonthNumberDisengagesAutonomous mileson public roads2014/09 0 4207.22014/10 2 23971.12014/11 4 15836.62014/12 2 9413.12015/01 1 18192.12015/02 0 18745.12015/03 1 22204.22015/04 1 31927.32015/05 0 38016.82015/06 0 42046.62015/07 0 34805.12015/08 0 38219.82015/09 0 36326.62015/10 0 47143.52015/11 2 43275.9Total 13 424331Summary of All Reportable DisengagementsTable 4 summarizesa ll disengagements required to be reported to the DMV, i.e., both those where a failure of the autonomous technology was detected and those involving drivers taking control when required for safe operation. A brief description of each reportable disengagement is shown in Appendix A.Table 4: All Reportable DisengagementsMonthNumberDisengagesAutonomous mileson public roads2014/09 2 4207.22014/10 19 23971.12014/11 21 15836.62014/12 43 9413.12015/01 53 18192.12015/02 14 18745.12015/03 30 22204.22015/04 51 31927.32015/05 13 38016.82015/06 11 42046.62015/07 29 34805.12015/08 7 38219.82015/09 16 36326.62015/10 16 47143.52015/11 16 43275.9Total 341 424331 Figure 3, below, shows the relationship between all reportable disengagements and the number of autonomous miles driven.Figure 3: Autonomous miles driven per reportable disengagementTable 5 below provides the breakdown of disengagements by cause. Note that, while we have used, where applicable, the causes mentioned in the DMV rule (weather conditions, road surface conditions, construction, emergencies, accidents or collisions), those causes were infrequent in our experience. Far more frequent were the additional causes we have labeled as unwanted maneuver, perception discrepancy, software discrepancy, hardware discrepancy, incorrect behavior prediction, or other road users behaving recklessly.4Table 5: Disengagements by Cause4 O ur cause descriptions reflect the categories of disengagements that our experience has taught us are the most useful for analyzing any underlying issue. “Recklessly behaving road user” indicates that our driver disengaged from autonomous mode to respond to reckless behavior by another driver, cyclist, or pedestrian. “Hardware discrepancy” indicates that a hardware element is not performing as expected. “Unwanted maneuver of the vehicle” involves the SDC moving in a way that is undesirable (e.g., coming uncomfortably close to a parked car). “Perception discrepancy” refers to a situation in which the SDC’s sensors are not correctly perceiving an object (e.g., perceiving overhanging branches as an obstacle). “Incorrect behavior prediction of other traffic participants” involves not correctly predicting the behavior of another road user (e.g., incorrectly predicting that pedestrians on the sidewalk will jaywalk). “Software discrepancy” covers situations involving apparent software inadequacies that do not readily fall into other categories (e.g., map or calibration issues).Cause Sep2014Oct2014Nov2014Dec2014Jan2015Feb2015Mar2015Apr2015May2015Jun2015Jul2015Aug2015Sep2015Oct2015Nov2015Totaldisengage forweatherconditionsduring testing 0 0 0 0 1 5 0 6 0 0 0 0 0 0 1 13 disengage for arecklesslybehaving roaduser 1 0 1 1 1 3 3 7 0 0 0 2 1 0 3 23 disengage forhardwarediscrepancy 0 1 0 0 2 1 0 1 0 5 8 1 8 8 4 39 disengage forunwantedmaneuver ofthe vehicle 0 3 6 14 15 1 3 2 1 0 3 2 0 3 2 55 disengage for aperceptiondiscrepancy 1 2 3 18 19 2 20 30 4 4 8 0 4 3 1 119 disengage forincorrectbehaviorprediction ofother trafficparticipants 0 2 2 0 1 0 2 0 0 0 0 0 0 1 0 8 disengage for asoftwarediscrepancy 0 11 9 9 14 2 1 5 8 2 9 2 3 1 4 80 disengage forconstructionzone duringtesting 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 3 disengage foremergencyvehicle duringtesting 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Total 2 19 21 43 53 14 30 51 13 11 29 7 16 16 16 341Table 6 provides information on the location of disengagements covered in this report.Table 6: Disengagements by LocationLocation Sep2014Oct2014Nov2014Dec2014Jan2015Feb2015Mar2015Apr2015May2015Jun2015Jul2015Aug2015Sep2015Oct2015Nov2015TotalInterstate 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 Freeway 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 4 Highway 0 1 2 0 1 1 4 2 3 2 2 2 5 4 3 32 Street 2 18 19 43 52 13 26 49 9 9 23 5 11 12 13 304 Total 2 19 21 43 53 14 30 51 13 11 29 7 16 16 16 341In its listing of possible disengagement causes, the DMV rule asks each manufacturer to state “whether the disengagement was the result of a planned test of the autonomous vehicle.” All thedisengagements reported here occurred during planned testing of the SDCs. However, if the rule isseeking information on whether the disengagement occurred during planned testing of thedisengagement function itself, we do not test that function on public roads. Instead, we test thefunction in our own facilities during vehicle preparation.Miles Driven by Autonomous VehiclesAppendix Bs hows the total number of miles each autonomous vehicle was tested in autonomous mode on public roads each month. The total miles driven on public roads in Californiaby Google’s fleet during the period, broken down by autonomous and manual modes, is shown inFigure 4.FIgure 4: Miles driven on public roads in California.Time Elapsed Between Technology Failure and Driver Assumption of ControlThe DMV rule requires that our report include in our summary of disengagements the “period of time elapsed from when the autonomous vehicle test driver was alerted of the technology failure and the driver assumed manual control of the vehicle.” This requirement is relevant only to the “technology failure” category of disengagements when the vehicle hands over control to the driver for immediate action. Appendix A shows this elapsed time for each disengagement where the data are available. In the vast majority of cases, the driver took control in one second or less after the immediate manual control message was received. The average time of all measurable events was 0.84 seconds.Appendix ASummary of Each Reportable DisengagementDate Location Type Time to manual CauseSep 2014 Street Safe Operation - Disengage for a perception discrepancy Sep 2014 Street Safe Operation - Disengage for a recklessly behaving agent Oct 2014 Street Safe Operation - Disengage for a perception discrepancy Oct 2014 Street Failure Detection 0.7s Disengage for hardware discrepancyOct 2014 Street Safe Operation - Disengage for incorrect behavior prediction of other traffic participantsOct 2014 Street Failure Detection 0.8s Disengage for unwanted maneuver of the vehicle Oct 2014 Street Failure Detection 0.8s Disengage for unwanted maneuver of the vehicle Oct 2014 Street Failure Detection 0.9s Disengage for a software discrepancyOct 2014 Street Safe Operation - Disengage for a perception discrepancyOct 2014 Highway Failure Detection 0.6s Disengage for a software discrepancyOct 2014 Street Failure Detection 0.9s Disengage for a software discrepancyOct 2014 Street Failure Detection 0.9s Disengage for a software discrepancyOct 2014 Street Failure Detection 1.0s Disengage for a software discrepancyOct 2014 Street Failure Detection 0.6s Disengage for a software discrepancyOct 2014 Street Failure Detection 0.9s Disengage for a software discrepancyOct 2014 Street Failure Detection 0.6s Disengage for a software discrepancyOct 2014 Street Failure Detection 0.6s Disengage for a software discrepancyOct 2014 Street Safe Operation - Disengage for unwanted maneuver of the vehicleOct 2014 Street Safe Operation - Disengage for incorrect behavior prediction of other traffic participantsOct 2014 Street Failure Detection 0.7s Disengage for a software discrepancy Oct 2014 Street Failure Detection * Disengage for a software discrepancy Nov 2014 Street Failure Detection 0.5s Disengage for a software discrepancy Nov 2014 Highway Failure Detection 0.8s Disengage for a software discrepancy Nov 2014 Street Failure Detection 0.7s Disengage for a software discrepancy Nov 2014 Street Failure Detection 0.2s Disengage for a software discrepancy Nov 2014 Street Failure Detection 0.7s Disengage for a software discrepancyNov 2014 Street Safe Operation - Disengage for a perception discrepancyNov 2014 Street Failure Detection 0.2s Disengage for incorrect behavior prediction of other traffic participantsNov 2014 Street Failure Detection 0.8s Disengage for a software discrepancyNov 2014 Street Failure Detection 0.6s Disengage for a software discrepancyNov 2014 Street Safe Operation - Disengage for unwanted maneuver of the vehicleNov 2014 Street Failure Detection * Disengage for incorrect behavior prediction of other traffic participantsNov 2014 Street Safe Operation - Disengage for a recklessly behaving agentNov 2014 Street Failure Detection 0.7s Disengage for unwanted maneuver of the vehicle Nov 2014 Street Safe Operation - Disengage for unwanted maneuver of the vehicle Nov 2014 Street Safe Operation - Disengage for a perception discrepancyNov 2014 Street Failure Detection 0.2s Disengage for unwanted maneuver of the vehicle Nov 2014 Highway Failure Detection 1.1s Disengage for a software discrepancyNov 2014 Street Safe Operation - Disengage for a perception discrepancyNov 2014 Street Failure Detection 2.2s Disengage for unwanted maneuver of the vehicle Nov 2014 Street Safe Operation - Disengage for unwanted maneuver of the vehicle Nov 2014 Street Failure Detection 2.2s Disengage for a software discrepancyDec 2014 Street Failure Detection 0.2s Disengage for a software discrepancyDec 2014 Street Safe Operation - Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection * Disengage for a software discrepancyDec 2014 Street Failure Detection 1.8s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.7s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.8s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection * Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.3s Disengage for a perception discrepancyDec 2014 Street Failure Detection 1.2s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.8s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.3s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 1.1s Disengage for a perception discrepancyDec 2014 Street Failure Detection 1.7s Disengage for a perception discrepancyDec 2014 Street Failure Detection 1.1s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection * Disengage for a perception discrepancyDec 2014 Street Failure Detection * Disengage for a perception discrepancyDec 2014 Street Failure Detection 0.3s Disengage for a software discrepancyDec 2014 Street Failure Detection 1.0s Disengage for a perception discrepancyDec 2014 Street Failure Detection * Disengage for a perception discrepancyDec 2014 Street Failure Detection 0.7s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.6s Disengage for a perception discrepancyDec 2014 Street Failure Detection * Disengage for a perception discrepancyDec 2014 Street Failure Detection 1.3s Disengage for a perception discrepancyDec 2014 Street Failure Detection 0.4s Disengage for a software discrepancyDec 2014 Street Failure Detection 0.2s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 2.0s Disengage for a perception discrepancyDec 2014 Street Failure Detection 0.8s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.8s Disengage for a software discrepancyDec 2014 Street Failure Detection 1.6s Disengage for a software discrepancyDec 2014 Street Failure Detection 0.8s Disengage for a perception discrepancyDec 2014 Street Failure Detection 0.3s Disengage for a software discrepancyDec 2014 Street Failure Detection 1.7s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.3s Disengage for unwanted maneuver of the vehicle Dec 2014 Street Failure Detection 0.4s Disengage for a recklessly behaving agentDec 2014 Street Failure Detection 0.2s Disengage for a perception discrepancyDec 2014 Street Failure Detection 1.2s Disengage for a software discrepancyDec 2014 Street Failure Detection * Disengage for a perception discrepancyDec 2014 Street Safe Operation - Disengage for construction zone during testing Dec 2014 Street Safe Operation - Disengage for a perception discrepancyDec 2014 Street Failure Detection 0.6s Disengage for a perception discrepancyDec 2014 Street Failure Detection * Disengage for a perception discrepancyDec 2014 Street Failure Detection 1.3s Disengage for a perception discrepancyJan 2015 Street Failure Detection 1.9s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection * Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.2s Disengage for a perception discrepancyJan 2015 Street Failure Detection * Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.2s Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.5s Disengage for a software discrepancyJan 2015 Street Failure Detection 0.3s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 0.3s Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.8s Disengage for a software discrepancyJan 2015 Street Failure Detection 0.3s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 0.8s Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.5s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 0.7s Disengage for a perception discrepancyJan 2015 Street Failure Detection * Disengage for adverse road surface conditions such as road holes or bumpsJan 2015 Street Failure Detection 0.4s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 0.3s Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.7s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection * Disengage for a software discrepancyJan 2015 Street Failure Detection 0.3s Disengage for a perception discrepancyJan 2015 Street Failure Detection 1.0s Disengage for a software discrepancyJan 2015 Street Failure Detection 0.4s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 1.4s Disengage for a perception discrepancyJan 2015 Street Failure Detection 1.9s Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.3s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 0.2s Disengage for a software discrepancyJan 2015 Street Failure Detection 0.2s Disengage for a software discrepancyJan 2015 Street Failure Detection 1.0s Disengage for a software discrepancyJan 2015 Street Failure Detection 2.0s Disengage for a software discrepancyJan 2015 Street Failure Detection 0.2s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 0.8s Disengage for a perception discrepancyJan 2015 Highway Safe Operation - Disengage for a recklessly behaving agentJan 2015 Street Failure Detection 0.2s Disengage for a perception discrepancyJan 2015 Street Safe Operation - Disengage for incorrect behavior prediction of other traffic participantsJan 2015 Street Failure Detection 0.3s Disengage for a software discrepancyJan 2015 Street Failure Detection 1.4s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 1.3s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection 0.9s Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection * Disengage for unwanted maneuver of the vehicle Jan 2015 Street Failure Detection * Disengage for a perception discrepancyJan 2015 Street Failure Detection 0.6s Disengage for a software discrepancy。

无人驾驶

兼首席执行官李彦宏坐在一辆林肯汽车的副驾驶座位上，去参加2017百度AI开发者大会上，视频中驾驶座位驾驶员没有碰触方向盘。在视频中，李彦宏向大家描述了乘坐自动驾驶汽车的实时体验：“现在车非常的多，但是还是很平稳，感觉非常不错。前面也有一个屏幕可以展示出来自动驾驶汽车探索的周边的装只用了三天时间（Apollo1.0）。
2019年四季度上市
蔚来ES8
蔚来对产品的定义，ES8 将拥有特斯拉的性能、雷克萨斯的质量以及接近汉兰达的价格. 蔚来宣布 ES8 价格，基准版补贴前为 44.8 万价格，创始版补贴前价格为 54.8 万元。 ES8 基于电池租用方案价格，基准版补贴前为 34.8 万价格，创始版补贴前价格为 44.8 万元。 ES8 是全球首个安装Mobileye EyeQ4芯片的车型，EyeQ4的处理能力是EyeQ3的8倍，平均功耗5瓦，响应时间20毫秒。蔚来加入了国家电网的超级充电（2017年 12月31日总数42000根，2020年预计12万根），都可以为蔚来的车型进行充电。3分钟换电池。 2017年12月16日上市，ES8，7座SUV
0月底，与金龙合作率先实现无人驾驶小巴车的小规模量产及试运营，并在 2019年与江淮、北汽，2020年与奇7.24
22015.12.10无人驾驶汽车项目启动无人车进行了第一次路测
3
2016.9.1
42017.04.17无人车获得美国加州政府颁发的全球第 15 张无人车上路测试牌照。
042015.12.10 无人车第一次路测18无人车的优势
6、节省时间：开车时间&拥堵时间麦肯锡公司估计，无人驾驶汽车每天为全球司机节省的时间总和高达10亿个小时。整个城市都依靠导航地图来运行的场景。汽车之间会相互合作，改道出行避免堵车。堵车将成为过去时，人们能更快到达目的地。

智能汽车无人驾驶原理知识培训

Part
3、光测距系统 LIDAR
① 谷歌采用了Velodyne公司的车顶激光测距系统。扫描器发射64束覆盖汽车周围360°角内的区域距离可以精确到2cm以内的激光射线，然后激光碰到车辆周围的物体，又反射回来，这样就计算出了物体的距离。
② 另一套在底部的系统测量出车辆在三个方向上的加速度、角速度等数据，然后再结合GPS数据计算出车辆的位置，所有这些数据与车载摄像机捕获的图像一起输入计算机，软件以极高的速度处理这些数据。这样，系统就可以非常迅速的作出判断。
汽车技术培训-
19
汽车技术培训-
THANKS
汽车技术培训-智能汽车无人驾驶原理知识培训20源自Part汽车技术培训-
6、全球定位系统GPS
① 一个自动驾驶员需要知道他正在去哪儿。谷歌使用Applanix公司的定位系统，以及他们自己的研制开发Google Map和GPS 技术。
汽车技术培训-智能汽车无人驾驶原理知识培训
16
3
Part
7、轮角度编码器 Wheel Encoder
① 轮载传感器可以在Google汽车穿梭于车流中时测量它的速度。进而自动调节汽车的行驶速度。帮助汽车在地图上找到准确的位置。
2
1 无人驾驶概述
Part
汽车技术培训-
无人驾驶汽车是一种智能汽车，也可以称之为轮式移动机器人，主要依靠车内的以计算机系统为主的智能驾驶仪来实现无人驾驶。
无人驾驶汽车是通过车载传感系统感知道路环境，自动规划行车路线并控制车辆到达预定目标的智能汽车。
无人驾驶汽车集自动控制、体系结构、人工智能、视觉计算等众多技术于一体，是计算机科学、模式识别和智能控制技术高度发展的产物，也是衡量一个国家科研实力和工业水平的一个重要标志，在国防和国民经济领域具有广阔的应用前景。

未卜先知的谷歌：15年前的14个先见之明

未卜先知的谷歌：15年前的14个先见之明导读：如今的谷歌已与15年前截然不同——从一家默默无名的创业公司，成长为今天互联网科技产业的巨头。

国外科技网站在Young Entrepreneur Council上发问：您认为谷歌取得今天成功的主要原因是什么？对创业公司有何启示？谷歌巨大成功的关键，是为员工创造了无与伦比的工作环境。

广告是谷歌企业元素中重要的一部分。

谷歌的根基或许永远都是搜索，而这也是互联网上最有效也最能吸引用户的方式。

北京时间12月16日消息，据TheNextWeb网站报道，今秋早些时候，谷歌喜迎15岁“生日”。

如今的谷歌已与15年前截然不同——从一家默默无名的创业公司，成长为今天互联网科技产业的巨头。

国外科技网站在Young Entrepreneur Council上发问：您认为谷歌取得今天成功的主要原因是什么？对创业公司有何启示？下面是笔者获得的答案摘编：1、内部企业家精神（Intrapreneurship）谷歌正常运行，就可以催生出层出不穷、自由流动（free-flowing）的新产品与工艺，产业内，无人能出其右。

谷歌用20%的时间，伫立在创新竞赛的巅峰，留住最聪明的人为公司献才献智。

2、让消费者和广告商满意大多数公司像谷歌一样，取得成功不光靠单一用户的贡献。

例如，谷歌就有消费者用户和商业用户。

谷歌兼顾了这两个群体，使他们获得了满意的使用体验。

谷歌不仅提供了最好的搜索引擎，还有世界上最高效的在线广告产品。

你怎么看谷歌的成功？如果目前你的公司只有一种类型的客户，我保证还有其他客户可以成为你的“贵人”。

关注他们，他们就会关注你3、不停发问谷歌总是走在科技的前沿。

今天看来理所当然的状况，从未使谷歌止步，因为它在数年前就在思考，什么是可行的。

谷歌CEO拉里·佩奇经常提到“登月（moon shot）”工程的重要性，无人驾驶汽车、增强现实（augmented reality）也常被提及。

《智能网联汽车技术概论》课件 - 第一章-智能网联汽车技术综述

• 2003年，国防科技大学与一汽合作的红旗CA7460实现了高速公路自动驾驶示范，最高时速170Km/ 小时，可以实现自动超车。
• 2011年7月，国防科技大学自主研发的红旗HQ3无人驾驶汽车首次完成了长沙至武汉286Km的高速全程无人奥林匹克森林公园”路线上来回行驶，吸引了无数眼球。
• 2011年，内华达州率先通过了汽车驾驶汽车立法，解决了州公路上自驾汽车的路试问题。
No.10008
0
2
• 智能网联汽车的发展趋势
No.10008
国外智能网联汽车的发展现状
• 1.美国自动驾驶技术发展
• 在美国、欧洲、日本等发达国家和地区，自动驾驶技术是未来交通发展的重要方向。在技术研发、道路测试、标准法规和政策等方面，为智能网联汽车的发展提供了条件。为了加快自动驾驶商业化的政策支持，我国在这方面的研究也很活跃，为自动驾驶技术的开发和测试创造了坚实的基础。
• 在智能化层面，汽车配备了多种传感器（摄像头、超声波雷达、毫米波雷达、激光雷达），实现对周围环境的自主感知，通过一系列传感器信息识别和决策操作，汽车按照预定控制算法的速度与预设定交通路线规划的寻径轨迹行
• 驶在。网联化层面，车辆采用新一代移动通信技术（LTE-V、5G等），实现车辆位置信息、车速信息、外部信息等车辆信息之间的交互，并由控制器进行计算，通过决策模块计算后控制车辆按照预先设定的指令行驶，进一步增强车辆的智能化程度和自动驾驶能力。
人与系统
人
自动驾驶系统（“系统”）监控驾驶环境
车道内正常行驶，人高速公路无车道干
涉路段，泊车工况。
高速公路及市区无
人
车道干涉路段，换道、环岛绕行、拥

无人驾驶课件

未来展望
无人驾驶的商业前景
快递用车和工业应用
快递用车和“列队”卡车将是另一个可能较快采用无人车的领域。在线购物和电子商务网站快速兴起, 给快递公司带来利好。人们喜欢在网上订购物品（如食品、货物和服务）, 几小时就能送货上门。中国电商 2015 年销售总额达到 5900 亿美元, 很多产品承诺同日送达。这促进了电动车和卡车快递。
未来展望
04
未来展望
序幕刚起
无人驾驶作为人工智能的一个重大应用发现从来就不是某一项单一的技术, 它是众多技术的整合。它需要有算法上的创新、系统上的融合, 以及来自云平台的支持。无人驾驶序幕刚启, 其中有着千千万万的机会亟待发掘。在此背景之下, 过去的几年中, 自动驾驶产业化在多个方面取得了很大进步, 其中合作共享已成为共识, 产业链不断整合, 业界企业相继开展合作, 传感器价格将不断下降, 预计在 2020 年, 将有真正意义上的无人车面市。
发展现状
预测和回应人类的行为
当车辆需要在楼房建设区域、事故区域或是其他会有人通过手势信号来指挥行车的区域穿行时, 无人驾驶汽车也将面临难题。
这需要汽车可以精确地观察停车标志、交通信号灯、限速标牌、其他车辆的行为以及人类驾驶员会关注的其他通用信息, 以判断以什么样的速度行车, 以及何时何地需要转弯等。
过去状况
国内发展状况
• 2011 年, 同样是由国防科技大学研制的红旗HQ3 无人驾驶汽车, 首次完成了从长沙到武汉286 km 的高速全程无人驾驶试验, 实测全程自主驾驶平均时速 87 km。
• 2012 年11 月底, 一辆由军事交通学院研制的无人驾驶智能汽车从京津高速台湖收费站启程, 用一个小时左右到达天津东丽收费站, 全程行驶104 km, 成功完成高速公路测试, 成为我国第一辆官方认证完成高速公路测试的无人驾驶智能汽车。

无人驾驶汽车的发展现状与展望

无人驾驶汽车的发展现状与展望课程名称：无人驾驶车辆设计理论学生姓名：张原旗、周昕、王铭轩、张妍、王浩淼、于骁机械与车辆学院0引言近年来，互联网技术的迅速发展给汽车工业带来了革命性变化的机会。

与此同时，汽车智能化技术正逐步得到广泛应用，这项技术使汽车的操作更简单，行驶安全性也更好，而其中最典型也是最热门的未来应用就是无人驾驶汽车。

也许这一趋势能使无人汽车比新能源汽车更早走入大众的生活。

无人驾驶车辆从广义上可以分为地面、空中、水上和水下等多种形式，但现阶段一般特指所有地面无人驾驶载体，它包括军用平台和民用平台，地面无人驾驶车辆起源于军事需求，无人驾驶车辆在军事应用领域的迅猛发展，极大地促进了世界各国研发无人驾驶车辆的热情。

无人驾驶车辆具有异常广阔的应用前景。

通过车辆与车辆(V2V)以及车辆与基础设施（V2I）的通信，可以实现无人驾驶车辆与其他车辆、基础设施以及人类之间的交互。

凭借这种优势，多个无人驾驶车辆之间可以完成编队，通过交叉口、多任务分配等多种方式的协作，从而形成一种全新的智能交通方式。

同时在一些工作环境恶劣、劳动强度较大的领域，如矿区环境，无人驾驶车辆也已崭露头角；另外，无人驾驶车辆还可以应用在军事领域，节省人力，提高作战效率，减少人员伤亡。

汽车的智能化发展是逐步推进的，2014年美国汽车工程师学会（SAE）将汽车自动化等级定义为以下六个层次：L0无自动驾驶（Level0 DriverOnly）：完全由驾驶员控制汽车的速度和方向，没有辅助系统的干预。

L1辅助驾驶（Level1 Assisted）：驾驶员持续控制着汽车的纵向或横向的驾驶任务，另一方向的驾驶任务由辅助驾驶系统控制，如辅助泊车系统。

L2部分自动驾驶（Level2 Partial Automation）:驾驶员必须持续监测动态驾驶任务及驾驶环境。

在一定的条件下，自动驾驶系统控制汽车的纵向和横向动态驾驶任务，如交通拥堵辅助系统。

L3有条件自动驾驶（Level3 Condition Automation）：驾驶员不需要持续监测动态驾驶任务和驾驶环境，但是驾驶员必须时刻处于一个可以随时恢复对汽车控制的位置。

无人驾驶车辆

什么要发展无人驾驶车辆二、无人驾驶车辆国内外发展现状三、无人驾驶车辆关键技术
环境感知技术
A
Key point
定位与导航技术
B
C
控制技术
3.1环境感知技术
1
• 视觉技术
2
• 激光雷达技术
3
• 毫米波雷达技术
4
• 超声波技术
1
• 视觉技术
机器视觉采用摄影机和电脑代替人眼的方式，对目标进行识别、跟踪和测量。在无人车辆上，通过机器视觉应用，可解释交通信号、交通图案、道路标识等环境语言。与其它传感器相比，机器视觉具有检测信息大、价格相对低廉等优点；但在复杂环境下，要将探测的目标与背景提取出来，具有图像计算量大、算法不易实现等缺点。机器视觉又分为单目视觉、全景视觉和立体视觉。
谷歌无人驾驶车
2.2国内现状
我国在军用无人车辆方面的研发尚处起步阶段。 2015 年，中国兵器工业集团北方车辆研究所成立的地面无人平台研发中心，展示了其最新研制的军用无人车，其采用6×6分布式轮毂电机驱动形式，可实现零半径速差转向。据报道，该车具有单兵跟踪的能力，可携带物资随队行军，此外该车辆还可搭载专门的探测设备，以辅助士兵完成侦察等任务。
4
• 超声波技术
超声波指的是工作频率在20KHz以上的机械波，它具有穿透性强、衰减小、反射能力强等特点。超声波测距原理是利用测量超声波发射脉冲和接收脉冲的时间差，再结合超声波在空气中传输的速度来计算距离。现阶段广泛应用于倒车雷达系统中的便是超声波测距，且现在国内外市场上大量存在的泊车辅助系统大都采用超声波测距系统。
激光雷达测距
3
• 毫米波雷达技术
毫米波雷达工作在毫米波波段，其频域为30GHz~300GHz之间，波长介于厘米波和光波之间，兼有微波制导和光电制导的优点。毫米波导引头体积小、质量轻、空间分辨率高；穿透雾、烟、灰尘的能力强，具有全天候(大雨天除外)、全天时的特点。然而，雨雾对毫米波的影响非常大，吸收强度大。在雨雾天气，毫米波雷达的性能将会大大下降。目前，毫米波雷达主要应用于有人车辆的碰撞预警和防撞等主动安全应用，在无人车辆领域的应用相对激光雷达较少；毫米波雷达可以探测一定区域内的所有目标，但是其方向性较激光雷达差，且测量精度也不如激光雷达；另外，相对于一般的二维激光雷达，其成本高昂。这些因素虽然限制了毫米波雷达在无人车辆上的应用，但许多国内外无人车辆，仍然会安装一个毫米波雷达用于探测车辆正前方的障碍。

无人驾驶技术

1 引言汽车的发展已经有100多年的历史了, 它的出现大大节约了人类的出行时间和出行成本。

但随着社会的发展, 人口的增加, 汽车数量呈现爆发式的增长。

这导致了交通拥堵、环境污染、能源危机、交通事故频发, 给城市建设和提升带来了阻碍。

从汽车的发展我们可以看出, 尽管汽车经过了一个世纪的发展, 但汽车的行驶模式从未发生过本质的变化。

在行驶过程中, 驾驶员通过视觉反馈了解道路的情况, 对行驶方向进行控制, 这就形成了一个“车-路-驾驶员”的闭环系统。

在这个系统中, 驾驶员是控制的核心。

但在实际的车辆行驶过程中, 驾驶员会受到很多未知因素的干扰, 具有不稳定性, 这种传统的车辆行驶方式缺点日益突出, 这也是目前交通事故频发的主要原因。

同时不同驾驶员操作习惯和行为方式不统一也是造成车辆拥堵的主要原因。

据统计, 2017年因为交通事故死亡人数达6.3万人, 而且造成事故发生原因九成以上都是人为原因。

因此剔除驾驶员的不稳定因素成为提高驾驶安全和效率的一个发展方向。

无人驾驶技术应运而生, 无人汽车通过车辆上装备的传感装置感知周围环境, 利用人工智能技术模拟人类的驾驶习惯和处理紧急事故的应对方式, 避免了人类在极端条件下心理压力对行为能力的影响的缺陷, 这使得汽车具有自主行驶能力, 让汽车的行驶变的安全可靠。

2 无人驾驶技术简介随着人工智能 (AI) 的发展, 人们也开始把眼光聚焦到无人驾驶技术领域。

汽车制造商、汽车出行服务商甚至是专业导航服务商都敏锐地意识到无人驾驶技术可能带来的巨大商机。

目前, 把无人车运营列入远期商业目标的公司包括巨头级别的企业如Google、滴滴, 初创型的企业如Pony.ai (小马智行) 、Roadstar.ai (星行科技) 等。

这些平台都试图及早占领无人驾驶出行服务市场, 在未来“去司机化”服务领域抢先占据有利高地。

美国谷歌公司是最先发展无人驾驶汽车的公司, 并且在2017年11月率先进行了不配备安全驾驶员的无人驾驶汽车的测试。

谷歌Waymo：已实现全自动无人驾驶

０成为世界上最新一名１０００￣Ｚ，美元富翁。黑色星期五” 乐观情绪的推动，在线零售逊的股价上涨逾２％，贝佐斯的财富也应声
刹车，以及在必要时可将车辆带到安全地方的计
算力和动力。谷歌是全球最早开展无人驾驶技术研发的公司之一，负责技术研发的正是公司旗下的Ｗａｙｍｏ项目团队。２０１６￣９１２月，Ａｌｐｈａｂｅｔ宣布将Ｗａｙｍｏ独立出来成为一家新的分公司。
在投资者日的演示中，可口可乐首席增长官弗朗西斯科・克雷斯波（ＦｒａｎｃｉｓｃｏＣｒｅｓｐｏ）向在场的听众介绍了一个全新的概念：增长科学。“ 增
长不是目标 … －－这是一门学问。 ” 他说， “ 当你去实
展业务，坦白说，产品在一个国家卖得好并不算什
么，只有在一个以上的大型国家取得成功才算是
果就是：利润率高于竞争对手。 “ 与其告诉消费者他们应该喝什么，不如虚心地去了解他们的口味和需求，调整我们自己的
在百度宣布２０１８年量产无人车之后，全球无人驾驶领域另一大巨头一一Ｗａｙｍ０也公布了最
新的无人车商用计划。
１１月２４日，Ａｌｐｈａｂｅｔ（谷歌母公司）旗下的Ｗａｙｍ０正式宣布，他们的汽车实现了完全自动无人驾驶（车上没有人类驾驶员），并且将在未来几个月推出新的叫车服务。

无人驾驶概况及技术简析可编辑全文

，然后对这些区域提取特征，最后使用训练的分类器进行分类
基于深度学习目框提取速度
标检测的热潮
CVPR 2014 R-CNN
NIPS 2015 Faster R-CNN
CVPR 2016 YOLO
SSD
SSD300： 74.3% mAP
63.4% mAP 46fps
DPM（HOG+SVM） 66% mAP 0.02fps
2011 年，柏林自由大学顺利完成拥堵交通流、交通信号灯及环岛通行等诸多项目。
2015年，google无人车完成美国加州公路测试。
21世纪
2007
2011
2015
2003
2003 ，清华大学研制成功 THMR-V 型无人驾驶车辆。
2009
2009年，Google已完成多款无人驾驶样车，以及近100万公里的实际道路测试。
1月
7月
2016 年 1 月，初创公司 Nauto 使用行车记录仪实现ADAS功能。
2016年9月，Uber 在匹兹堡市向公众开放无人驾驶汽车出行服务。
2016年12月， Chris Urmson成立了自己的自动驾驶创业公司。
2017年1月，Quanergy 公司的Solid State LiDAR S3获得了汽车无人类的最高奖项。
25
2.2 关键技术：目标感知基于深度学习的视觉和LiDAR数据融合方法
1) 显著提高识别分类精度以及收敛速度； 2) 采用车载NVIDIA TX1（15W）运算可达120帧/秒； 3) 物体识别率提高将近5%
26
2.2 关键技术：目标感知
27

是否应该禁止使用无人驾驶汽车辩论辩题

是否应该禁止使用无人驾驶汽车辩论辩题正方观点，应该禁止使用无人驾驶汽车。

首先，无人驾驶汽车存在着严重的安全隐患。

根据美国国家公路交通安全管理局的数据，2018年无人驾驶汽车在测试中发生了多起致命事故，这表明无人驾驶汽车的技术还不够成熟，存在着严重的安全风险。

而且，无人驾驶汽车无法像人类司机一样做出复杂的判断和决策，容易导致交通事故的发生。

其次，无人驾驶汽车的普及将导致大量的工作岗位流失。

根据一项研究显示，如果无人驾驶汽车得到普及，将有数百万的司机岗位将被取代，这将对社会造成严重的影响，增加失业人数，导致社会不稳定。

最后，无人驾驶汽车的普及还将对道路交通带来严重的拥堵问题。

因为无人驾驶汽车的技术还不够成熟，无法像人类司机一样适应各种复杂的交通环境，这将导致道路交通更加拥堵，影响城市的交通运行效率。

反方观点，不应该禁止使用无人驾驶汽车。

首先，无人驾驶汽车可以提高交通安全性。

根据美国交通安全局的数据显示，90%的交通事故是由人为因素导致的，而无人驾驶汽车可以通过先进的传感器和人工智能技术来避免这些事故的发生，从而提高交通安全性。

其次，无人驾驶汽车可以提高交通效率。

由于无人驾驶汽车可以通过互联网和人工智能技术进行实时通信和协调，可以更加高效地规划路线和避开交通拥堵，从而提高道路交通的效率。

最后，无人驾驶汽车可以为社会创造更多的就业机会。

虽然无人驾驶汽车可能会取代一部分司机的工作，但是与此同时，无人驾驶汽车的研发和生产将会为社会创造更多的就业机会，促进经济的发展。

总的来说，尽管无人驾驶汽车存在一些问题，但是其优势远大于劣势，因此不应该禁止使用无人驾驶汽车。

名人名言，马斯克曾经表示，“无人驾驶汽车将会是未来的主流交通方式。

”这表明了无人驾驶汽车的重要性和发展前景。

经典案例，谷歌无人驾驶汽车在美国加利福尼亚州进行了长达数年的测试，取得了良好的成绩，证明了无人驾驶汽车的可行性和安全性。

《智能运输系统_智能驾驶电子道路图_数据模型与表达》系列国家标准解读

高精度地图——智能驾驶的基础支撑“智能驾驶电子道路图，又称‘高精度地图’，伴随着智能驾驶的发展应运而生，自从2009年谷歌无人驾驶团队采用基于高精度地图的自动驾驶方案以来，高精度地图开始被各大车企看作是未来发展自动驾驶的基础支撑。

”朱大伟对高精度地图的由来作出简要概括。

2014年前后，国内外汽车企业和自动驾驶方案商陆续提出自动驾驶发展规划，高精度地图的研发被各大地图厂商提上日程。

行业普遍认为高精度电子地图是智能驾驶的关键性基础技术，是否拥有高质量、高精度的电子地图将直接影响智能驾驶行业的发展。

2015年，智能驾驶高精度地图行业在中国处于发展的初期阶段，行业内没有高精度地图的相关标准，导致市面上的高精度地图存在精度不统一、模型不统一、表达不统一等问题，亟需规范高精度地图的模型和表达。

自此，高精度地图的发展成为自动驾驶行业关注的焦点，相关的政策与标准变化一直是行业热点话题。

2021年，中共中央、国务院印发的《国家综合立《智能运输系统智能驾驶电子道路图》系列国家标准解读文/ 李斌国家市场监督管理总局(国家标准化管理委员会)批准发布的《智能运输系统智能驾驶电子道路图数据模型与表达》系列标准，将于今年12月1日起实施。

该系列标准的发布对于推动智能驾驶、智慧交通等行业的发展及应用意义重大。

为进一步了解标准制定的背景、过程、亮点及意义，探讨标准即将发挥的作用及相关企业的应对措施，本刊邀请标准主要起草人之一、北京四维图新科技股份有限公司（以下简称“四维图新”）地图中心政策标准总监朱大伟对标准进行解读。

扫一扫阅读本栏目更多文章体交通网规划纲要》（以下简称《规划纲要》），以及交通运输部印发的《交通运输领域新型基础设施建设行动方案（2021—2025年）》中均将基础设施全要素、全周期数字化作为建设目标以及任务，并且在提升公路智慧化服务水平方面，对精准定位、车道级应用等高精度时空信息服务方面提出了要求。

同时在《规划纲要》的重点任务中再次明确提出要“构建高精度交通地理信息平台，加快各领域建筑信息模型技术自主创新应用”。

自动驾驶汽车

2015年5月，谷歌在官方博客上宣布将于2015年夏天在加利福尼亚州山景城的公路上测试其自动驾驶汽车。谷歌称，公司自动驾驶原型车在山景城公路上测试时的最高时速将限制在25英里（约合40公里），每一辆原型车上将配备一位安全驾驶员，后者可以在任何时候通过车上的方向盘、刹车和油门控制汽车。谷歌表示，其自动驾驶汽车在公司测试装置中的累计行程接近100万英里（约合160万公里），每周大约增加1万英里（约合1.6万公里）。这意味着，谷歌自动驾驶汽车拥有大量可以利用的经验，相当于“人类大约75年的驾龄”。
死亡事故
2016年6月30日，美国特斯拉汽车公司证实，一辆该公司生产的S型电动轿车在自动驾驶模式下发生撞车事故，导致司机身亡。美国负责监管公路交通安全的机构正在对事故车辆的自动驾驶系统展开调查。这是美国首例涉及汽车自动驾驶功能的交通死亡事故。
事故于2016年5月7日发生在美国佛罗里达州，导致涉事S型电动轿车车主、一名40岁美国男子身亡。特斯拉在官方博客中说，公司在事发后立即向美国国家高速公路交通安全管理局作了报告。
文件明确了自动驾驶汽车申请临时上路行驶的相关条件。
第一，申请上路测试人需是在中国境内注册的独立法人单位，因进行自动驾驶相关科研、定型试验，可申请临时上路行驶。测试车辆必须符合《机动车运行安全技术条件》（GB7258）标准。测试车辆具备自动、人工两种驾驶模式，并可随时切换；测试车辆必须安装相应监管装置，能监测驾驶行为和车辆位置。
第二，测试车辆上路前必须先在封闭测试场内按相关标准进行测试和考核，考核结果经专家评审，通过后才允许上路测试。
研发历史
2009年，曝光了自动驾驶汽车的雏形图片。
2009年曝光的自动驾驶汽车雏形2010年10月9日，谷歌公司在官方博客中宣布，正在开发自动驾驶汽车，目标是通过改变汽车的基本使用方式，协助预防交通事故，将人们从大量的驾车时间中解放出来，并减少碳排放。

无人驾驶的测试和验证

无人驾驶的测试和验证一、引言与背景无人驾驶作为智能交通系统的重要组成部分，其起源可以追溯到20世纪70年代。

随着计算机技术、传感器技术、人工智能等领域的快速发展，无人驾驶汽车逐渐从概念走向现实。

近年来，谷歌、特斯拉等科技巨头以及各大汽车制造商的加入，使得无人驾驶技术得到了前所未有的关注和投入。

研究无人驾驶具有重要的现实意义和深远的社会影响。

首先，无人驾驶可以有效减少交通事故，提高道路通行效率，缓解交通拥堵。

据数据显示，90%以上的交通事故都是由人为因素造成的，无人驾驶技术有望从根本上解决这个问题。

其次，无人驾驶有助于实现能源节约和环境保护，减少碳排放。

此外，无人驾驶还可以为弱势群体提供便捷、安全的出行服务，如老年人、残疾人和儿童。

从经济角度来看，无人驾驶技术的发展将带来产业链的重构，创造大量新的就业机会，同时推动相关产业如保险、房地产、物流等领域的变革。

从科技角度来看，无人驾驶技术的突破将推动人工智能、大数据、物联网等关键技术的发展，进而推动整个社会的科技进步。

二、行业/领域的核心概念与分类核心概念无人驾驶是指利用计算机、传感器、控制系统等设备，使汽车在没有人类司机的情况下自主完成驾驶任务。

其核心目标是实现车辆的安全、高效、舒适行驶。

分类与特征1.根据传感器类型，无人驾驶车辆可以分为：–仅使用摄像头：主要依靠计算机视觉技术进行感知，如谷歌无人驾驶汽车。

–使用摄像头和雷达：结合两种传感器的优势，提高感知准确性，如特斯拉自动驾驶系统。

–使用摄像头、雷达和激光雷达：全方位感知周围环境，如百度Apollo平台。

2.根据驾驶场景，无人驾驶车辆可以分为：–封闭场地无人驾驶：如工厂、机场、港口等特定场景。

–公开道路无人驾驶：在普通公路、高速公路等公开道路上行驶。

3.根据自动驾驶级别，无人驾驶车辆可以分为：–辅助驾驶：主要用于提高驾驶员的驾驶体验，如自适应巡航、车道保持辅助等。

–部分自动驾驶：在特定场景下，车辆可以自主完成部分驾驶任务，如停车、变道等。